

import math



def   __UpperCAmelCase					(      __a :     list						,__a :     int = 0						,__a :     int = 0      )     ->	list:

					"""simple docstring"""

					_a      :       int   =						end or len(__a      )
					for i in range(__a						,__a      ):
										_a      :       Optional[Any]   =						i
										_a      :       str   =						array[i]
										while temp_index != start and temp_index_value < array[temp_index - 1]:
															_a      :       Union[str, Any]   =						array[temp_index - 1]
															temp_index -= 1
										_a      :       Optional[Any]   =						temp_index_value
					return array



def   __UpperCAmelCase					(      __a :     list						,__a :     int						,__a :     int      )     ->	None:  # Max Heap

					"""simple docstring"""

					_a      :       Any   =						index
					_a      :       Any   =						2 * index + 1  # Left Node
					_a      :       Optional[int]   =						2 * index + 2  # Right Node

					if left_index < heap_size and array[largest] < array[left_index]:
										_a      :       Union[str, Any]   =						left_index

					if right_index < heap_size and array[largest] < array[right_index]:
										_a      :       str   =						right_index

					if largest != index:
										_a ,   _a      :       Tuple   =						array[largest], array[index]
										heapify(__a						,__a						,__a      )



def   __UpperCAmelCase					(      __a :     list      )     ->	list:

					"""simple docstring"""

					_a      :       Dict   =						len(__a      )

					for i in range(n // 2						,-1						,-1      ):
										heapify(__a						,__a						,__a      )

					for i in range(n - 1						,0						,-1      ):
										_a ,   _a      :       List[str]   =						array[0], array[i]
										heapify(__a						,0						,__a      )

					return array



def   __UpperCAmelCase					(      __a :     list						,__a :     int						,__a :     int						,__a :     int      )     ->	int:

					"""simple docstring"""

					if (array[first_index] > array[middle_index]) != (
					    array[first_index] > array[last_index]
					):
										return array[first_index]
					elif (array[middle_index] > array[first_index]) != (
					    array[middle_index] > array[last_index]
					):
										return array[middle_index]
					else:
										return array[last_index]



def   __UpperCAmelCase					(      __a :     list						,__a :     int						,__a :     int						,__a :     int      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						low
					_a      :       List[Any]   =						high
					while True:
										while array[i] < pivot:
															i += 1
										j -= 1
										while pivot < array[j]:
															j -= 1
										if i >= j:
															return i
										_a ,   _a      :       int   =						array[j], array[i]
										i += 1



def   __UpperCAmelCase					(      __a :     list      )     ->	list:

					"""simple docstring"""

					if len(__a      ) == 0:
										return array
					_a      :       int   =						2 * math.ceil(math.loga(len(__a      )      )      )
					_a      :       Tuple   =						16
					return intro_sort(__a						,0						,len(__a      )						,__a						,__a      )



def   __UpperCAmelCase					(      __a :     list						,__a :     int						,__a :     int						,__a :     int						,__a :     int      )     ->	list:

					"""simple docstring"""

					while end - start > size_threshold:
										if max_depth == 0:
															return heap_sort(__a      )
										max_depth -= 1
										_a      :       Dict   =						median_of_a(__a						,__a						,start + ((end - start) // 2) + 1						,end - 1      )
										_a      :       int   =						partition(__a						,__a						,__a						,__a      )
										intro_sort(__a						,__a						,__a						,__a						,__a      )
										_a      :       int   =						p
					return insertion_sort(__a						,__a						,__a      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			a__												=						input('''Enter numbers separated by a comma : ''').strip()
			a__												=						[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))











'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




